Abstract: Apparatuses, methods and storage medium for computing including determination of work placement on processor cores are disclosed herein. In embodiments, an apparatus may include one or more processors, devices, and/or circuitry to identify a favored core of the processor cores. The one or more processors, devices, and/or circuitry may be configured to determine whether to migrate a thread to or from the favored core. In some embodiments, the determination may be by a process executed by a driver and/or by an algorithm executed by a power control unit of the processor.

Abstract: Systems and methods are disclosed to implement a neural network training system to train a multitask neural network (MNN) to generate a low-dimensional entity representation based on a sequence of events associated with the entity. In embodiments, an encoder is combined with a group of decoders to form a MNN to perform different machine learning tasks on entities. During training, the encoder takes a sequence of events in and generates a low-dimensional representation of the entity. The decoders then take the representation and perform different tasks to predict various attributes of the entity. As the MNN is trained to perform the different tasks, the encoder is also trained to generate entity representations that capture different attribute signals of the entities. The trained encoder may then be used to generate semantically meaningful entity representations for use with other machine learning systems.

Abstract: Described herein is a method of low-bitrate coding of audio data and generating enhancement metadata for controlling audio enhancement of the low-bitrate coded audio data at a decoder side, including the steps of: (a) core encoding original audio data at a low bitrate to obtain encoded audio data; (b) generating enhancement metadata to be used for controlling a type and/or amount of audio enhancement at the decoder side after core decoding the encoded audio data; and (c) outputting the encoded audio data and the enhancement metadata. Described is further an encoder configured to perform said method. Described is moreover a method for generating enhanced audio data from low-bitrate coded audio data based on enhancement metadata and a decoder configured to perform said method.

Abstract: Described herein is a method for setting up a decoder for generating processed audio data from an audio bitstream, the decoder comprising a Generator of a Generative Adversarial Network, GAN, for processing of the audio data, wherein the method includes the steps of (a) pre-configuring the Generator for processing of audio data with a set of parameters for the Generator, the parameters being determined by training, at training time, the Generator using the full concatenated distribution; and (b) pre-configuring the decoder to determine, at decoding time, a truncation mode for modifying the concatenated distribution and to apply the determined truncation mode to the concatenated distribution. Described are further a method of generating processed audio data from an audio bitstream using a Generator of a Generative Adversarial Network, GAN, for processing of the audio data and a respective apparatus. Moreover, described are also respective systems and computer program products.

Abstract: Described herein is a method of processing an audio signal using a deep-learning-based generator, wherein the method includes the steps of: (a) inputting the audio signal into the generator for processing the audio signal; (b) mapping a time segment of the audio signal to a latent feature space representation, using an encoder stage of the generator; (c) upsampling the latent feature space representation using a decoder stage of the generator, wherein at least one layer of the decoder stage applies sinusoidal activation; and (d) obtaining, as an output from the decoder stage of the generator, a processed audio signal. Described are further a method for training said generator and respective apparatus, systems and computer program products.

Abstract: Apparatuses, methods and storage medium for computing including determination of work placement on processor cores are disclosed herein. In embodiments, an apparatus may include one or more processors, devices, and/or circuitry to identify a favored core of the processor cores. The one or more processors, devices, and/or circuitry may be configured to determine whether to migrate a thread to or from the favored core. In some embodiments, the determination may be by a process executed by a driver and/or by an algorithm executed by a power control unit of the processor.

Abstract: Embodiments are directed to a companding method and system for reducing coding noise in an audio codec. A method of processing an audio signal includes the following operations. A system receives an audio signal. The system determines that a first frame of the audio signal includes a sparse transient signal. The system determines that a second frame of the audio signal includes a dense transient signal. The system compresses/expands (compands) the audio signal using a companding rule that applies a first companding exponent to the first frame of the audio signal and applies a second companding exponent to the second frame of the audio signal, each companding exponent being used to derive a respective degree of dynamic range compression and expansion for a corresponding frame. The system then provides the companded audio signal to a downstream device.

Abstract: Respective initial feature sets are obtained for the nodes of a graph in which the nodes represent instances of entity types and edges represent relationships. Using the initial feature sets and the graph, a graph convolutional model is trained to generate one or more types of predictions. In the model, a representation of a particular node at a particular hidden layer is based on aggregated representations of neighbor nodes, and an embedding produced at a final hidden layer is used as input to a prediction layer. The trained model is stored.

Abstract: Described herein is a method of determining parameters for a generative neural network for processing an audio signal, wherein the generative neural network includes an encoder stage mapping to a coded feature space and a decoder stage, each stage including a plurality of convolutional layers with one or more weight coefficients, the method comprising a plurality of cycles with sequential processes of: pruning the weight coefficients of either or both stages based on pruning control information, the pruning control information determining the number of weight coefficients that are pruned for respective convolutional layers; training the pruned generative neural network based on a set of training data; determining a loss for the trained and pruned generative neural network based on a loss function; and determining updated pruning control information based on the determined loss and a target loss. Further described are corresponding apparatus, programs, and computer-readable storage media.

Abstract: Described herein is a method of generating, in a dynamic range reduced domain, an enhanced multi-channel audio signal from an audio bitstream including a multi-channel audio signal, wherein the multi-channel audio signal comprises two or more channels, and wherein the method includes jointly enhancing the two or more channels of the dynamic range reduced raw multi-channel audio signal using a multi-channel Generator of a Generative Adversarial Network setting. Described herein are further a method for training a multi-channel Generator in a dynamic range reduced domain in a Generative Adversarial Network setting, an apparatus for generating, in a dynamic range reduced domain, an enhanced multi-channel audio signal from an audio bitstream including a multi-channel audio signal, respective systems and a computer program product.

Abstract: Respective initial feature sets are obtained for the nodes of a graph in which the nodes represent instances of entity types and edges represent relationships. Using the initial feature sets and the graph, a graph convolutional model is trained to generate one or more types of predictions. In the model, a representation of a particular node at a particular hidden layer is based on aggregated representations of neighbor nodes, and an embedding produced at a final hidden layer is used as input to a prediction layer. The trained model is stored.

Abstract: Respective initial feature sets are obtained for the nodes of a graph in which the nodes represent instances of entity types and edges represent relationships. Using the initial feature sets and the graph, a graph convolutional model is trained to generate one or more types of predictions. In the model, a representation of a particular node at a particular hidden layer is based on aggregated representations of neighbor nodes, and an embedding produced at a final hidden layer is used as input to a prediction layer. The trained model is stored.

Abstract: Embodiments are directed to a companding method and system for reducing coding noise in an audio codec. A compression process reduces an original dynamic range of an initial audio signal through a compression process that divides the initial audio signal into a plurality of segments using a defined window shape, calculates a wideband gain in the frequency domain using a non-energy based average of frequency domain samples of the initial audio signal, and applies individual gain values to amplify segments of relatively low intensity and attenuate segments of relatively high intensity. The compressed audio signal is then expanded back to the substantially the original dynamic range that applies inverse gain values to amplify segments of relatively high intensity and attenuating segments of relatively low intensity. A QMF filterbank is used to analyze the initial audio signal to obtain a frequency domain representation.

Abstract: Described herein is a method of waveform decoding, the method including the steps of: (a) receiving, by a waveform decoder, a bitstream including a finite bitrate representation of a source signal; (b) waveform decoding the finite bitrate representation of the source signal to obtain a waveform approximation of the source signal; (c) providing the waveform approximation of the source signal to a generative model that implements a probability density function, to obtain a probability distribution for a reconstructed signal of the source signal; and (d) generating the reconstructed signal of the source signal based on the probability distribution. Described are further a method and system for waveform coding and a method of training a generative model.

Abstract: Apparatuses, methods and storage medium for computing including determination of work placement on processor cores are disclosed herein. In embodiments, an apparatus may include one or more processors, devices, and/or circuitry to identify a favored core of the processor cores. The one or more processors, devices, and/or circuitry may be configured to determine whether to migrate a thread to or from the favored core. In some embodiments, the determination may be by a process executed by a driver and/or by an algorithm executed by a power control unit of the processor.

Abstract: Embodiments are directed to a companding method and system for reducing coding noise in an audio codec. A method of processing an audio signal includes the following operations. A system receives an audio signal. The system determines that a first frame of the audio signal includes a sparse transient signal. The system determines that a second frame of the audio signal includes a dense transient signal. The system compresses/expands (compands) the audio signal using a companding mle that applies a first companding exponent to the first frame of the audio signal and applies a second companding exponent to the second frame of the audio signal, each companding exponent being used to derive a respective degree of dynamic range compression and expansion for a corresponding frame. The system then provides the companded audio signal to a downstream device.

Abstract: Embodiments are directed to a companding method and system for reducing coding noise in an audio codec. A compression process reduces an original dynamic range of an initial audio signal through a compression process that divides the initial audio signal into a plurality of segments using a defined window shape, calculates a wideband gain in the frequency domain using a non-energy based average of frequency domain samples of the initial audio signal, and applies individual gain values to amplify segments of relatively low intensity and attenuate segments of relatively high intensity. The compressed audio signal is then expanded back to the substantially the original dynamic range that applies inverse gain values to amplify segments of relatively high intensity and attenuating segments of relatively low intensity. A QMF filterbank is used to analyze the initial audio signal to obtain a frequency domain representation.

Abstract: Apparatuses, methods and storage medium for computing including determination of work placement on processor cores are disclosed herein. In embodiments, an apparatus may include one or more processors, devices, and/or circuitry to identify a favored core of the processor cores. The one or more processors, devices, and/or circuitry may be configured to determine whether to migrate a thread to or from the favored core. In some embodiments, the determination may be by a process executed by a driver and/or by an algorithm executed by a power control unit of the processor.

Abstract: A multicore processor may include multiple processing cores that were previously designated as active cores and at least one processing core that was previously designated as a functional spare. The processor may include an interface to receive, during operation of the processor in an end-user environment, a request to change the designation of at least one of the processing cores. The processor may be to store, into a desired cores configuration data structure in response to the request, data representing a bitmask that reflects the requested change, and to execute a reset sequence. During the reset sequence, the processor may activate, dependent on the bitmask, a processing core previously designated as a functional spare, or may deactivate, dependent on the bitmask, a processing core previously designated as an active core. The processor may include a predetermined maximum number of active cores and a predetermined minimum number of functional spares.

Abstract: Described herein is a method of generating a media bitstream to transmit parameters for updating a neural network implemented in a decoder, wherein the method includes the steps of: (a) determining at least one set of parameters for updating the neural network; (b) encoding the at least one set of parameters and media data to generate the media bitstream; and (c) transmitting the media bitstream to the decoder for updating the neural network with the at least one set of parameters. Described herein are further a method for updating a neural network implemented in a decoder, an apparatus for generating a media bitstream to transmit parameters for updating a neural network implemented in a decoder, an apparatus for updating a neural network implemented in a decoder and computer program products comprising a computer-readable storage medium with instructions adapted to cause the device to carry out said methods when executed by a device having processing capability.